Mitigating the choice of the duration in DDMS models through a parametric link

TL;DR

This paper introduces a new methodology to address the challenge of selecting the duration hyper-parameter in duration-dependent Markov-switching models, enhancing forecasting accuracy without heuristic assumptions.

Contribution

The paper proposes a parametric link approach to mitigate duration choice in DDMS models, supported by Monte Carlo simulations and empirical S&P 500 volatility forecasting.

Findings

The methodology improves forecasting performance in DDMS models.

Monte Carlo simulations validate the effectiveness of the approach.

Empirical results demonstrate practical applicability to financial data.

Abstract

One of the most important hyper-parameters in duration-dependent Markov-switching (DDMS) models is the duration of the hidden states. Because there is currently no procedure for estimating this duration or testing whether a given duration is appropriate for a given data set, an ad hoc duration choice must be heuristically justified. In this paper, we propose and examine a methodology that mitigates the choice of duration in DDMS models when forecasting is the goal. Two Monte Carlo simulations, based on classical applications of DDMS models, are employed to evaluate the methodology. In addition, an empirical investigation is carried out to forecast the volatility of the S\&P 500, which showcases the capabilities of the proposed model.